Multiple ErbB-2/Neu Phosphorylation Sites Mediate Transformation through Distinct Effector Proteins
Bibliographic record
Abstract
Amplification of the type I receptor tyrosine kinase ErbB-2 (HER2/Neu) is observed in 20-30% of human mammary carcinomas, correlating with a poor clinical prognosis. We have previously demonstrated that four (Tyr(1144), Tyr(1201), Tyr(1226/1227), or Tyr(1253)) of the five known Neu/ErbB-2 autophosphorylation sites can independently mediate transforming signals. The transforming potential of at least two of these autophosphorylation sites (Tyr(1144) and Tyr(1226/1227)) has been further correlated with their ability to associate with Grb2 and Shc adapter proteins, respectively. To confirm the specificity of these interactions, we have created a series of second site mutants in these phosphorylation sites. The results showed that Grb2 recruitment to site 1144 is absolutely required for transforming signal from this autophosphorylation site, whereas association of Shc-mediated transformation is dependent on conservation of the NPXY motif spanning Tyr(1227). A stretch of amino acid identity around tyrosines 1201 (ENPEYLTP)and 1253 (ENPEYLDL) exists, and mutation of key residues within this motif reveals distinct requirements for an intact protein tyrosine-binding protein (NPXY). We show that DOK-R, a protein tyrosine-binding site-containing protein implicated in Ras signaling, interacts with Neu/ErbB-2 at Tyr(1253) as do two unidentified proteins, p150 and p34, the latter correlating with transformation. Together these data argue that ErbB-2/Neu is capable of mediating transformation through distinct effector pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".